Methods and apparatus for monitoring an audience of media based on thermal imaging

ABSTRACT

Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes a thermal image detector to determine a heat blob count based on a frame of thermal image data, the frame of thermal image data captured in the media environment, a comparator to compare the heat blob count to a prompted people count, the prompted people count based on one or more responses to a prompting message, and when the heat blob count and the prompted people count match, cause a timer that is to trigger generation of the prompting message to be reset.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience monitoring, and, moreparticularly, to methods and apparatus for monitoring an audience ofmedia based on thermal imaging.

BACKGROUND

Media monitoring companies, also referred to as audience measuremententities, monitor user interaction with media devices, such assmartphones, tablets, laptops, smart televisions, etc. To facilitatesuch monitoring, monitoring companies enlist panelists and installmeters at the media presentation locations of those panelists. Themeters monitor media presentations and transmit media monitoringinformation to a central facility of the monitoring company. Such mediamonitoring information enables the media monitoring companies to, amongother things, monitor exposure to advertisements, determineadvertisement effectiveness, determine user behavior, identifypurchasing behavior associated with various demographics, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example audience measurement system having anexample meter to monitor an example media presentation environment.

FIG. 2 illustrates a block diagram of the example meter of FIG. 1 togenerate exposure data for the media.

FIG. 3 illustrates a block diagram of an example people identifierincluded in the example meter of FIG. 2 to determine a people count foraudience monitoring data.

FIG. 4 illustrates a block diagram of an example thermal image detectorincluded in the example people identifier of FIG. 3 to determine a heatblob count.

FIG. 5 illustrates a block diagram of an example people identificationmodel controller included in the example people identifier of FIG. 3 totrain a model to determine a people count of the media presentationenvironment of FIG. 1.

FIG. 6 is a flowchart representative of machine readable instructionswhich may be executed to implement an example people meter controllerincluded in the example people identifier of FIG. 3 to determine apeople count.

FIGS. 7 and 8 are flowcharts representative of machine readableinstructions which may be executed to implement the example thermalimage detector of FIGS. 3 and/or 4 to determine a heat blob count.

FIG. 9 is a flowchart representative of machine readable instructionswhich may be executed to implement an example comparator included in theexample people identifier of FIG. 3 to verify a people count of themedia presentation environment of FIG. 1.

FIG. 10 is a flowchart representative of machine readable instructionswhich may be executed to implement the example people identificationcontroller of FIGS. 3 and/or 5 to train the model to determine a peoplecount of the media presentation environment of FIG. 1.

FIG. 11 is a block diagram of an example processing platform structuredto execute the instructions of one or more of FIGS. 6-10 to implementthe example people identifier of FIGS. 2-5.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

Descriptors “first,” “second,” “third,” etc. are used herein whenidentifying multiple elements or components which may be referred toseparately. Unless otherwise specified or understood based on theircontext of use, such descriptors are not intended to impute any meaningof priority, physical order or arrangement in a list, or ordering intime but are merely used as labels for referring to multiple elements orcomponents separately for ease of understanding the disclosed examples.In some examples, the descriptor “first” may be used to refer to anelement in the detailed description, while the same element may bereferred to in a claim with a different descriptor such as “second” or“third.” In such instances, it should be understood that suchdescriptors are used merely for ease of referencing multiple elements orcomponents.

DETAILED DESCRIPTION

The meters described above implement media identification features andpeople identification features. Such features (e.g., mediaidentification and people identification) enable the generation of mediamonitoring information that can be used for determining audienceexposure (also referred to as user exposure) to advertisements,determining advertisement effectiveness, determining user behaviorrelative to media, identifying the purchasing behavior associated withvarious demographics, etc. The people identification features of themeter determine the audience in a media presentation environment. Forexample, the people identification feature may be implemented by activepeople meters, passive people meters, and/or a combination of activepeople meters and passive people meters to determine a people count.

An active people meter obtains a people count by actively prompting anaudience to enter information for audience member identification. Insome examples, an active people meter identifies an audience member bythe audience member's assigned panelist number or visitor number. Forexample, the active people meter obtains the assigned panelist number orvisitor number through a communication channel. In some examples, theactive people meter pairs the information corresponding to the audienceinput with a household (e.g., a specific media environment) and with thecorresponding demographic data for the people of that household. In someexamples, the active people meter validates viewership (e.g., the numberof audience members viewing media in the media environment) at a setinterval. For example, active people meter generates a prompting messagefor the audience members to verify that they are still in the audience.In this manner, the active people meter relies on audience compliance.In some examples, maintaining audience compliance is a challenge. Forexample, audience members may incorrectly enter the number of peopleviewing the media and/or they may miss the prompting messages generatedby the active people meter.

A passive people meter obtains audience information passively, usuallyby capturing images of the audience using a camera and then employingfacial recognition to identify the individual audience members includedin the audience. In some examples, image processing for facialrecognition may be inaccurate depending on the positioning of the cameracapturing the audience images. Additionally, the audience members may beuncomfortable having a camera watch them in their household environment.

To enable an accurate and less invasive method of determining a peoplecount, example methods and apparatus disclosed herein utilize acombination of a passive people meter and an active people meter. Anexample passive people meter disclosed herein is implemented by anexample thermal imaging system to identify audience members in the mediaenvironment. In some examples, the thermal imaging system is implementedby a thermal imaging camera to scan the media environment and captureframes of thermal image data of the media environment. The examplepassive people meter utilizes the frames of thermal image data to detectand count human size heat blobs in the media environment.

Example methods and apparatus disclosed herein additionally utilize theactive people meter to compare audience input with the heat blob counts.An example people identifier, implemented by the example active peoplemeter and the example passive people meter as disclosed herein, includesa comparator to compare the number of audience members that responded toa prompting message with a number of heat blobs detected by the passivepeople meter. In some examples, when the comparator determines thecounts do not match, the comparator can notify the active people meterto generate a new prompting message for the audience member. In otherexamples, when the comparator determines the counts do not match, thecomparator can inform a media measurement data controller to removeinaccurate counts determined by the active people meter.

In some examples, the example people identifier reduces (e.g.,minimizes) the amount of prompting generated by the active people meterrelative to prior people meters that do not employ a combination ofactive and passive people metering. For example, when the comparatordetermines the counts are a match, the audience count is verified, andsubsequent active prompting can be disabled for at least a givenmonitoring interval (e.g., 5 minutes, 15 minutes, etc.). Using thermalimaging, the people identifier monitors the media environment over time(e.g., continuously, at sampled time intervals, etc.) to determine thenumber of human size heat blobs present, validate against the numberentered on the active people meter, and accurately measure the audience.

In some examples disclosed herein, the example people identifierincludes a people identification model controller to train a model tolearn about the corresponding household media environment. As usedherein, a model is a description of an environment using mathematicalconcepts and language. A model is generally composed of relationshipsand variables, the relationships describing operators (e.g., such asalgebraic operators, functions, etc.) and the variables describingmonitored environment parameters of interest that can be quantified. Insome examples, the model is a machine learning and/or artificialintelligence (AI) model such as a Linear Regression model, a decisiontree, a support vector machine (SVM) model, a Naïve Bayes model, etc.

In some examples, the people identification model controller obtainsdata from the comparator, the active people meter, and the passivepeople meter and generates a feature vector corresponding to the data.The example people identification model controller utilizes the featurevector to train the model. For example, the feature vector includes datarepresentative of descriptive characteristics of a physical environment(e.g., the household media environment). In some examples, such dataincludes a date and time, a number of audience members present in themedia environment, a media source (e.g., radio media, television media,pay per view media, movies, Internet Protocol Television (IPTV),satellite television (TV), Internet radio, satellite radio, digitaltelevision), a media channel (e.g., broadcast channel, a domain name),and the demographics of the audience members. In this manner, theexample people identification model can generate the model to learn whowill be in the audience and at what time. Eventually, when training iscomplete, the model can be deployed at the meter and utilized to makeinformed decisions about the audience count.

In some examples, the model can utilize the presence of peopledetermined by the thermal imaging camera as a metric to determinewhether the people identifier is actually counting the right number ofpeople. For example, the model could be used to determine if theaudience views the same or similar media every Tuesday night at 8:00 pm,if there usually two people present in the media audience, etc. In suchan example, the model is used to verify the accuracy of the people countbased on the information obtained.

FIG. 1 is an illustration of an example audience measurement system 100having an example meter 102 to monitor an example media presentationenvironment 104. In the illustrated example of FIG. 1, the mediapresentation environment 104 includes panelists 106, 107, and 108, anexample media device 110 that receives media from an example mediasource 112, and the meter 102. The meter 102 identifies the mediapresented by the media device 110 and reports media monitoringinformation to an example central facility 114 of an audiencemeasurement entity via an example gateway 116 and an example network118. The example meter 102 of FIG. 1 sends media monitoring data and/oraudience monitoring data to the central facility 114 periodically,a-periodically and/or upon request by the central facility 114.

In the illustrated example of FIG. 1, the media presentation environment104 is a room of a household (e.g., a room in a home of a panelist, suchas the home of a “Nielsen family”) that has been statistically selectedto develop media (e.g., television) ratings data for apopulation/demographic of interest. In the illustrated example of FIG.1, the example panelists 106, 107 and 108 of the household have beenstatistically selected to develop media ratings data (e.g., televisionratings data) for a population/demographic of interest. People becomepanelists via, for example, a user interface presented on a media device(e.g., via the media device 110, via a website, etc.). People becomepanelists in additional or alternative manners such as, for example, viaa telephone interview, by completing an online survey, etc. Additionallyor alternatively, people may be contacted and/or enlisted using anydesired methodology (e.g., random selection, statistical selection,phone solicitations, Internet advertisements, surveys, advertisements inshopping malls, product packaging, etc.). In some examples, an entirefamily may be enrolled as a household of panelists. That is, while amother, a father, a son, and a daughter may each be identified asindividual panelists, their viewing activities typically occur withinthe family's household.

In the illustrated example, one or more panelists 106, 107 and 108 ofthe household have registered with an audience measurement entity (e.g.,by agreeing to be a panelist) and have provided their demographicinformation to the audience measurement entity as part of a registrationprocess to enable associating demographics with media exposureactivities (e.g., television exposure, radio exposure, Internetexposure, etc.). The demographic data includes, for example, age,gender, income level, educational level, marital status, geographiclocation, race, etc., of a panelist. While the example mediapresentation environment 104 is a household, the example mediapresentation environment 104 can additionally or alternatively be anyother type(s) of environments such as, for example, a theater, arestaurant, a tavern, a retail location, an arena, etc.

In the illustrated example of FIG. 1, the example media device 110 is atelevision. However, the example media device 110 can correspond to anytype of audio, video, and/or multimedia presentation device capable ofpresenting media audibly and/or visually. In some examples, the mediadevice 110 (e.g., a television) may communicate audio to another mediapresentation device (e.g., an audio/video receiver) for output by one ormore speakers (e.g., surround sound speakers, a sound bar, etc.). Asanother example, the media device 110 can correspond to a multimediacomputer system, a personal digital assistant, a cellular/mobilesmartphone, a radio, a home theater system, stored audio and/or videoplayed back from a memory such as a digital video recorder or a digitalversatile disc, a webpage, and/or any other communication device capableof presenting media to an audience (e.g., the panelists 106, 107 and108).

The media source 112 may be any type of media provider(s), such as, butnot limited to, a cable media service provider, a radio frequency (RF)media provider, an Internet based provider (e.g., IPTV), a satellitemedia service provider, etc. The media may be radio media, televisionmedia, pay per view media, movies, Internet Protocol Television (IPTV),satellite television (TV), Internet radio, satellite radio, digitaltelevision, digital radio, stored media (e.g., a compact disk (CD), aDigital Versatile Disk (DVD), a Blu-ray disk, etc.), any other type(s)of broadcast, multicast and/or unicast medium, audio and/or video mediapresented (e.g., streamed) via the Internet, a video game, targetedbroadcast, satellite broadcast, video on demand, etc.

The example media device 110 of the illustrated example shown in FIG. 1is a device that receives media from the media source 112 forpresentation. In some examples, the media device 110 is capable ofdirectly presenting media (e.g., via a display) while, in otherexamples, the media device 110 presents the media on separate mediapresentation equipment (e.g., speakers, a display, etc.). Thus, as usedherein, “media devices” may or may not be able to present media withoutassistance from a second device. Media devices are typically consumerelectronics. For example, the media device 110 of the illustratedexample could be a personal computer such as a laptop computer, and,thus, capable of directly presenting media (e.g., via an integratedand/or connected display and speakers). In some examples, the mediadevice 110 can correspond to a television and/or display device thatsupports the National Television Standards Committee (NTSC) standard,the Phase Alternating Line (PAL) standard, the Système Électronique pourCouleur avec Mémoire (SECAM) standard, a standard developed by theAdvanced Television Systems Committee (ATSC), such as high definitiontelevision (HDTV), a standard developed by the Digital VideoBroadcasting (DVB) Project, etc. Advertising, such as an advertisementand/or a preview of other programming that is or will be offered by themedia source 112, etc., is also typically included in the media. While atelevision is shown in the illustrated example, any other type(s) and/ornumber(s) of media device(s) may additionally or alternatively be used.For example, Internet-enabled mobile handsets (e.g., a smartphone, aniPod®, etc.), video game consoles (e.g., Xbox®, PlayStation 3, etc.),tablet computers (e.g., an iPad®, a Motorola™ Xoom™, etc.), digitalmedia players (e.g., a Roku® media player, a Slingbox®, a Tivo®, etc.),smart televisions, desktop computers, laptop computers, servers, etc.may additionally or alternatively be used.

The example meter 102 detects exposure to media and electronicallystores monitoring information (e.g., a code detected with the presentedmedia, a signature of the presented media, an identifier of a panelistpresent at the time of the presentation, a timestamp of the time of thepresentation) of the presented media. The stored monitoring informationis then transmitted back to the central facility 114 via the gateway 116and the network 118. While the media monitoring information istransmitted by electronic transmission in the illustrated example ofFIG. 1, the media monitoring information may additionally oralternatively be transferred in any other manner, such as, for example,by physically mailing the meter 102, by physically mailing a memory ofthe meter 102, etc.

The meter 102 of the illustrated example of FIG. 1 combines audiencemeasurement data and people metering data. For example, audiencemeasurement data is determined by monitoring media output by the mediadevice 110 and/or other media presentation device(s), and audiencemonitoring data (also referred to as demographic data, people monitoringdata, etc.) is determined from people monitoring data provided to themeter 102. Thus, the example meter 102 provides dual functionality of acontent measurement meter to collect content measurement data and peoplemeter to collect and/or associate demographic information correspondingto the collected audience measurement data.

For example, the meter 102 of the illustrated example collects mediaidentifying information and/or data (e.g., signature(s), fingerprint(s),code(s), tuned channel identification information, time of exposureinformation, etc.) and people data (e.g., user identifiers, demographicdata associated with audience members, etc.). The media identifyinginformation and the people data can be combined to generate, forexample, media exposure data (e.g., ratings data) indicative ofamount(s) and/or type(s) of people that were exposed to specificpiece(s) of media distributed via the media device 110. To extract mediaidentification data, the meter 102 and/or the example audiencemeasurement system 100 extracts and/or processes the collected mediaidentifying information and/or data received by the meter 102, which canbe compared to reference data to perform source and/or contentidentification. Any other type(s) and/or number of media monitoringtechniques can be supported by the meter 102.

Depending on the type(s) of metering the meter 102 is to perform, themeter 102 can be physically coupled to the media device 110 or may beconfigured to capture signals emitted externally by the media device 110(e.g., free field audio) such that direct physical coupling to the mediadevice 110 is not required. For example, the meter 102 of theillustrated example may employ non-invasive monitoring not involving anyphysical connection to the media device 110 (e.g., via Bluetooth®connection, WIFI® connection, acoustic watermarking, etc.) and/orinvasive monitoring involving one or more physical connections to themedia device 110 (e.g., via USB connection, a High Definition MediaInterface (HDMI) connection, an Ethernet cable connection, etc.).

In examples disclosed herein, to monitor media presented by the mediadevice 110, the meter 102 of the illustrated example employs audiowatermarking techniques and/or signature based-metering techniques.Audio watermarking is a technique used to identify media, such astelevision broadcasts, radio broadcasts, advertisements (televisionand/or radio), downloaded media, streaming media, prepackaged media,etc. Existing audio watermarking techniques identify media by embeddingone or more audio codes (e.g., one or more watermarks), such as mediaidentifying information and/or an identifier that may be mapped to mediaidentifying information, into an audio and/or video component of themedia. In some examples, the audio or video component is selected tohave a signal characteristic sufficient to hide the watermark. As usedherein, the terms “code” or “watermark” are used interchangeably and aredefined to mean any identification information (e.g., an identifier)that may be inserted or embedded in the audio or video of media (e.g., aprogram or advertisement) for the purpose of identifying the media orfor another purpose such as tuning (e.g., a packet identifying header).As used herein “media” refers to audio and/or visual (still or moving)content and/or advertisements. To identify watermarked media, thewatermark(s) are extracted and used to access a table of referencewatermarks that are mapped to media identifying information.

Unlike media monitoring techniques based on codes and/or watermarksincluded with and/or embedded in the monitored media, fingerprint orsignature-based media monitoring techniques generally use one or moreinherent characteristics of the monitored media during a monitoring timeinterval to generate a substantially unique proxy for the media. Such aproxy is referred to as a signature or fingerprint, and can take anyform (e.g., a series of digital values, a waveform, etc.) representativeof any aspect(s) of the media signal(s)(e.g., the audio and/or videosignals forming the media presentation being monitored). A signature maybe a series of signatures collected in series over a timer interval. Agood signature is repeatable when processing the same mediapresentation, but is unique relative to other (e.g., different)presentations of other (e.g., different) media. Accordingly, the term“fingerprint” and “signature” are used interchangeably herein and aredefined herein to mean a proxy for identifying media that is generatedfrom one or more inherent characteristics of the media.

Signature-based media monitoring generally involves determining (e.g.,generating and/or collecting) signature(s) representative of a mediasignal (e.g., an audio signal and/or a video signal) output by amonitored media device and comparing the monitored signature(s) to oneor more references signatures corresponding to known (e.g., reference)media sources. Various comparison criteria, such as a cross-correlationvalue, a Hamming distance, etc., can be evaluated to determine whether amonitored signature matches a particular reference signature. When amatch between the monitored signature and one of the referencesignatures is found, the monitored media can be identified ascorresponding to the particular reference media represented by thereference signature that with matched the monitored signature. Becauseattributes, such as an identifier of the media, a presentation time, abroadcast channel, etc., are collected for the reference signature,these attributes may then be associated with the monitored media whosemonitored signature matched the reference signature. Example systems foridentifying media based on codes and/or signatures are long known andwere first disclosed in Thomas, U.S. Pat. No. 5,481,294, which is herebyincorporated by reference in its entirety.

For example, the meter 102 of the illustrated example senses audio(e.g., acoustic signals or ambient audio) output (e.g., emitted) by themedia device 110. For example, the meter 102 processes the signalsobtained from the media device 110 to detect media and/or sourceidentifying signals (e.g., audio watermarks) embedded in portion(s)(e.g., audio portions) of the media presented by the media device 110.To sense ambient audio output by the media device 110, the meter 102 ofthe illustrated example includes an acoustic sensor (e.g., amicrophone). In some examples, the meter 102 may process audio signalsobtained from the media device 110 via a direct cable connection todetect media and/or source identifying audio watermarks embedded in suchaudio signals. In some examples, the meter 102 may process audio signalsand/or video signals to generate respective audio and/or videosignatures from the media presented by the media device 110.

To generate exposure data for the media, identification(s) of media towhich the audience is exposed are correlated with people data (e.g.,presence information) collected by the meter 102. The meter 102 of theillustrated example collects inputs (e.g., audience monitoring data)representative of the identities of the audience member(s) (e.g., thepanelists 106, 107 and 108). In some examples, the meter 102 collectsaudience monitoring data by periodically or aperiodically promptingaudience members in the monitored media presentation environment 104 toidentify themselves as present in the audience. In some examples, themeter 102 responds to events (e.g., when the media device 110 is turnedon, a channel is changed, an infrared control signal is detected, etc.)by prompting the audience member(s) to self-identify. In some examples,the meter 102 determines a people count by utilizing an example thermalimaging sensor 124. For example, the thermal imaging sensor 124 capturesframes of thermal image data of the media presentation environment 104and provides the thermal image data to the meter 102 to detect humansized blobs of heat. In some examples, the meter 102 responds to events(e.g., when the media device 110 is turned on, a channel is changed, aninfrared control signal is detected, etc.) by prompting the thermalimaging sensor 124 to capture thermal images.

The example thermal imaging sensor 124 utilizes heat energy of anenvironment (e.g., the media presentation environment 104) to generatethermal image data. The example thermal imaging sensor 124 records thetemperature of various objects in the frame, and then assigns differentshades of colors to corresponding different temperatures (e.g., todifferent ranges of temperatures). In some such examples, a particularshade of color indicates how much heat is radiating off an objectcompared to the other objects in the frame. For example, the thermalimaging sensor 124 includes measuring devices that capture infraredradiation (e.g., microbolometers) associated with each pixel in theframe. The measuring devices then record the temperature of the pixeland assign the pixel to an appropriate color. In some examples, thethermal imaging sensor 124 is configured to assign a greyscale to pixelsin a frame, where white may be indicative of “hot” (e.g., the highesttemperature in the frame) and dark grey/black may be indicative of“cold” (e.g., the lowest temperature in the frame). In some examples,the thermal imaging sensor 124 is configured to assign warm and coolcolors (e.g., warm corresponding to red, orange, and yellow colors; coolcorresponding to green, blue, and purple colors) to the pixels in aframe, where the warm colors may be indicative of relatively highertemperatures and the cool colors may be indicative of relatively lowertemperatures. In some examples, a human radiates more heat (e.g., isassociated with a higher temperature) than inanimate objects, such as acouch or remote control device.

The audience monitoring data and the exposure data can then be compliedwith the demographic data collected from audience members such as, forexample, the panelists 106, 107 and 108 during registration to developmetrics reflecting, for example, the demographic composition of theaudience. The demographic data includes, for example, age, gender,income level, educational level, marital status, geographic location,race, etc., of the panelist.

In some examples, the meter 102 may be configured to receive panelistinformation via an example input device 122 such as, for example, aremote control, An Apple iPad®, a cell phone, etc.). In such examples,the meter 102 prompts the audience members to indicate their presence bypressing an appropriate input key on the input device 122. For example,the input device 122 may enable the audience member(s) (e.g., thepanelists 106, 107 and 108 of FIG. 1) and/or an unregistered user (e.g.,a visitor to a panelist household) to input information to the meter 102of FIG. 1. This information includes registration data to configure themeter 102 and/or demographic data to identify the audience member(s).For example, the input device 122 may include a gender input interface,an age input interface, and a panelist identification input interface,etc. Although FIG. 1 illustrates multiple input devices 122, the examplemedia presentation environment 104 may include an input device 122 withmultiple inputs for multiple panelists. For example, an input device 122can be utilized as a household input device 122 where panelists of thehousehold may have a corresponding input assigned to the.

The example gateway 116 of the illustrated example of FIG. 1 is a routerthat enables the meter 102 and/or other devices in the mediapresentation environment (e.g., the media device 110) to communicatewith the network 118 (e.g., the Internet.)

In some examples, the example gateway 116 facilitates delivery of mediafrom the media source 112 to the media device 110 via the Internet. Insome examples, the example gateway 116 includes gateway functionality,such as modem capabilities. In some other examples, the example gateway116 is implemented in two or more devices (e.g., a router, a modem, aswitch, a firewall, etc.). The gateway 116 of the illustrated examplemay communicate with the network 118 via Ethernet, a digital subscriberline (DSL), a telephone line, a coaxial cable, a USB connection, aBluetooth connection, any wireless connection, etc.

In some examples, the example gateway 116 hosts a Local Area Network(LAN) for the media presentation environment 104. In the illustratedexample, the LAN is a wireless local area network (WLAN), and allows themeter 102, the media device 110, etc. to transmit and/or receive datavia the Internet. Alternatively, the gateway 116 may be coupled to sucha LAN. In some examples, the gateway 116 may be implemented with theexample meter 102 disclosed herein. In some examples, the gateway 116may not be provided. In some such examples, the meter 102 maycommunicate with the central facility 114 via cellular communication(e.g., the meter 102 may employ a built-in cellular modem).

The network 118 of the illustrated example is a wide area network (WAN)such as the Internet. However, in some examples, local networks mayadditionally or alternatively be used. Moreover, the example network 118may be implemented using any type of public or private network, such as,but not limited to, the Internet, a telephone network, a local areanetwork (LAN), a cable network, and/or a wireless network, or anycombination thereof.

The central facility 114 of the illustrated example is implemented byone or more servers. The central facility 114 processes and stores datareceived from the meter 102. For example, the example central facility114 of FIG. 1 combines audience monitoring data and programidentification data from multiple households to generate aggregatedmedia monitoring information. The central facility 114 generates reportsfor advertisers, program producers and/or other interested parties basedon the compiled statistical data. Such reports include extrapolationsabout the size and demographic composition of audiences of content,channels and/or advertisements based on the demographics and behavior ofthe monitored panelists.

As noted above, the meter 102 of the illustrated example provides acombination of media (e.g., content) metering and people metering. Theexample meter 102 of FIG. 1 is a stationary device that may be disposedon or near the media device 110. The meter 102 of FIG. 1 includes itsown housing, processor, memory and/or software to perform the desiredaudience measurement and/or people monitoring functions.

In examples disclosed herein, an audience measurement entity providesthe meter 102 to the panelist 106, 107 and 108 (or household ofpanelists) such that the meter 102 may be installed by the panelist 106,107 and 108 by simply powering the meter 102 and placing the meter 102in the media presentation environment 104 and/or near the media device110 (e.g., near a television set). In some examples, more complexinstallation activities may be performed such as, for example, affixingthe meter 102 to the media device 110, electronically connecting themeter 102 to the media device 110, etc.

To identify and/or confirm the presence of a panelist present in themedia device 110, the example meter 102 of the illustrated exampleincludes an example display 132. For example, the display 132 providesidentification of the panelists 106, 107, 108 present in the mediapresentation environment 104. For example, in the illustrated example,the meter 102 displays indicia or visual indicators (e.g., illuminatednumerals 1, 2 and 3) identifying and/or confirming the presence of thefirst panelist 106, the second panelist 107 and the third panelist 108.

FIG. 2 illustrates a block diagram of the example meter 102 of FIG. 1 togenerate exposure data for the media. The example meter 102 of FIG. 2 iscoupled to the example thermal imaging sensor 124 of FIG. 1 to generateaccurate people counts based on frames of thermal image data collectedfrom the example thermal imaging sensor 124. The example meter 102 ofFIG. 2 includes an example image sensor 201, an example audio sensor202, an example media identifier 204, an example network communicator206, an example communication processor 208, an example peopleidentifier 210, an example media measurement data controller 212, and anexample data store 214.

The example image sensor 201 of the illustrated example of FIG. 2 is acamera. The example image sensor 201 receives light waves, such as thelight waves emitting from the example media device 110 and converts theminto signals that convey information. Additionally or alternatively, theexample image sensor 201 may be implemented by a line input connection,where the video and images presented by the example media device 110 arecarried over an audio/video network (e.g., HDMI cable) to the examplemeter 102. The example image sensor 201 may not be included in theexample meter 102. For example, it may not be necessary for the meter102 to utilize the image sensor 201 to identify media data. However, insome examples, the image sensor 201 can be utilized for detection ofmedia data.

The example audio sensor 202 of the illustrated example of FIG. 2 is amicrophone. The example audio sensor 202 receives ambient sound (e.g.,free field audio) including audible media presented in the vicinity ofthe meter 102. Additionally or alternatively, the example audio sensor202 may be implemented by a line input connection. The line inputconnection may allow an external microphone to be used with the meter102 and/or, in some examples, may enable the audio sensor 202 to bedirectly connected to an output of a media device 110 (e.g., anauxiliary output of a television, an auxiliary output of an audio/videoreceiver of a home entertainment system, etc.). Advantageously, themeter 102 is positioned in a location such that the audio sensor 202receives ambient audio produced by the television and/or other devicesof the media presentation environment 104 (FIG. 1) with sufficientquality to identify media presented by the media device 110 and/or otherdevices of the media presentation environment 104 (e.g., a surroundsound speaker system). For example, in examples disclosed herein, themeter 102 may be placed on top of the television, secured to the bottomof the television, etc.

The example media identifier 204 of the illustrated example of FIG. 2analyzes signals received via the image sensor 201 and/or audio receivedvia the audio sensor 202 and identifies the media being presented. Theexample media identifier 204 of the illustrated example outputs anidentifier of the media (e.g., media-identifying information) to themedia measurement data controller 212. In some examples, the mediaidentifier 204 utilizes audio and/or video watermarking techniques toidentify the media. Additionally or alternatively, the media identifier204 utilizes signature-based media identification techniques.

The example network communicator 206 of the illustrated example of FIG.2 is a communication interface configured to receive and/or otherwisetransmit corresponding communications to and/or from the centralfacility 114. In the illustrated example, the network communicator 206facilitates wired communication via an Ethernet network hosted by theexample gateway 116 of FIG. 1. In some examples, the networkcommunicator 206 is implemented by a Wi-Fi radio that communicates viathe LAN hosted by the example gateway 116. In other examples disclosedherein, any other type of wireless transceiver may additionally oralternatively be used to implement the network communicator 206. Inexamples disclosed herein, the example network communicator 206communicates information to the communication processor 208 whichperforms actions based on the received information. In other examplesdisclosed herein, the network communicator 206 may transmit mediameasurement information provided by the media measurement datacontroller 212 (e.g., data stored in the data store 214) to the centralfacility 114 of the media presentation environment 104.

The example communication processor 208 of the illustrated example ofFIG. 2 receives information from the network communicator 206 andperforms actions based on that received information. For example, thecommunication processor 208 packages records corresponding to collectedexposure data and transmits records to the central facility 114. Inexamples disclosed herein, the communication processor 208 communicateswith the people identifier 210 and/or a media measurement datacontroller 212 to transmit people count data, demographic data, etc., tothe network communicator 206.

The example people identifier 210 of the illustrated example of FIG. 2determines audience monitoring data representative of the number and/oridentities of the audience member(s) (e.g., panelists) present in themedia presentation environment 104. In the illustrated example, thepeople identifier 210 is coupled to the thermal imaging sensor 124. Insome examples, the people identifier 210 is coupled to the thermalimaging sensor 124 via a direct connection (e.g., Ethernet) or indirectcommunication through one or more intermediary components. In someexamples, the thermal imaging sensor 124 is included in (e.g.,integrated with) the meter 102. The example people identifier 210collects data from the example thermal imaging sensor 124 and data fromexample input device(s) 122 corresponding to audience monitoring data.The example people identifier 210 provides the audience monitoring datato the media measurement data controller 212 such that the audiencemonitoring data can be correlated with the media identification data tofacilitate an identification of which media was presented to whichaudience member (e.g., exposure data). The example people identifier 210is described in further detail below in connection with FIGS. 3, 4, and5.

The example media measurement data controller 212 of the illustratedexample of FIG. 2 receives media identifying information (e.g., a code,a signature, etc.) from the media identifier 204 and audience monitoringdata from the people identifier 210 and stores the received informationin the data store 214. The example media measurement data controller 212periodically and/or a-periodically transmits, via the networkcommunicator 206, the media measurement information stored in the datastore 214 to the central facility 114 for post-processing of mediameasurement data, aggregation and/or preparation of media monitoringreports. In some examples, the media measurement controller 312generates exposure data. For example, the media measurement controller312 correlates the media identifying information with audiencemonitoring data, as described above, to generate exposure data.

The example data store 214 of the illustrated example of FIG. 2 may beimplemented by any device for storing data such as, for example, flashmemory, magnetic media, optical media, etc. Furthermore, the data storedin the example data store 214 may be in any data format such as, forexample, binary data, comma delimited data, tab delimited data,structured query language (SQL) structures, etc. In the illustratedexample, the example data store 214 stores media identifying informationcollected by the media identifier 204 and audience monitoring datacollected by the people identifier 210.

FIG. 3 illustrates a block diagram of the example people identifier 210of FIG. 2, which is to determine a people count for audience monitoringdata in accordance with teachings of this disclosure. The example peopleidentifier 210 includes an example people meter controller 302, anexample interface 304, an example thermal image detector 306, an examplecomparator 308, an example people identification model controller 310,and an example model database 312.

The example people meter controller 302 of the illustrated example ofFIG. 3 obtains user input from example input device(s) 122. For example,the panelists 106, 107, and 108 may press a key on the respective inputdevice(s) 122 indicating they are in the room (e.g., the mediapresentation environment 104). In some examples, the panelists 106, 107,108 enter a username, password, and/or other information to indicate whothey are and that they are in the room viewing media. In some examples,the people meter controller 302 determines a people count based on theuser input obtained from the input device(s) 122. For example, thepeople meter controller 302 may determine that two people (e.g., two ofthe panelists 106, 107, 108) have indicated their presence in the mediapresentation environment 104. The example people meter controller 302provides the people count to the example comparator 308.

In some examples, the people meter controller 302 generates promptingmessages at periodic and/or aperiodic scheduling intervals. For example,the people meter controller 302 generates a prompting message to bedisplayed on the media device 110 and/or a display of the meter 102. Theprompting messages can include questions and/or requests for which thepanelists 106, 107, 108 are to respond to. For example, the people metercontroller 302 may generate a prompting message every 42 minutes (or atsome other interval) asking the panelists 106, 107, 108 if they arestill viewing the media. In some examples, the people meter controller302 generates a prompting message based on one or more events, such whenthe media device 110 is turned on, when the channel has changed, whenthe media source has changed, etc. In some examples, the people metercontroller 302 receives a trigger from the comparator 308 to determinewhether to generate prompting messages or not generate promptingmessages. The example people meter controller 302 communicates theprompting messages through the communication processor 208 of FIG. 2 tothe media device 110.

The example interface 304 of the illustrated example of FIG. 3communicates between the example thermal imaging sensor 124 and theexample thermal image detector 306. For example, the interface 304obtains data from the thermal imaging sensor 124 and provides the datato the example thermal image detector 306. In some examples, theinterface 304 obtains requests from the example thermal image detector306 and passes the requests to the example thermal imaging sensor 124.For example, the interface 304 enables communication between the thermalimaging sensor 124 and the thermal image detector 306. The exampleinterface 304 may be any type of interface, such as a network interfacecard (NIC), an analog-to-digital converter, a digital-to-analogconverter, Universal Serial Bus (USB), GigE, FireWire, Camera Link®,etc.

The example thermal image detector 306 of the illustrated example ofFIG. 3 obtains thermal image data from the example interface 304 anddetermines a heat blob count based on the thermal image data. Forexample, the thermal image detector 306 obtains frames of thermal imagedata from the thermal imaging sensor 124 via the interface 304 andanalyzes the frames. In some examples, the thermal image detector 306determines human sized blobs of heat, or human size heat blobs, in theframes. In some examples, the thermal image detector 306 analyzes theframes of thermal image data for temperature values and particularshapes to evaluate if the blobs are indicative of humans. The examplethermal image detector 306 generates a heat blob count based on thenumber of detected human size heat blobs and provides the count to theexample comparator 308. In some examples, the example thermal imagedetector 306 provides output information indicative of thermal imagedata analysis results to the example people identification modelcontroller 310. The example thermal image detector 306 is described infurther detail below in connection with FIG. 4.

The example comparator 308 of the illustrated example of FIG. 3 obtainsthe people count from the example people meter controller 302 (e.g.,also referred to as the prompted people count) and the heat blob countfrom the example thermal image detector 306 and compares the two countvalues. The example comparator 308 determines if the count values areequal in value. When the comparator 308 determines the count valuesmatch, then the people count is verified. In some examples, when thepeople count is verified, the example comparator 308 notifies theexample people meter controller 302 to reset a scheduling interval timer(e.g., a counter, clock, or other timing mechanisms) that initiate thegeneration of prompting messages. For example, if the people metercontroller 302 is configured to generate prompting messages every 42minutes, then a timer is set to 42 minutes. When the timer expires, aprompting message is triggered. However, if the comparator 308 verifiesthe number of people (e.g., the panelists 106, 107, 108) in the mediapresentation environment 104, a prompting message is not needed todetermine whether people are still viewing the media.

In some examples, the comparator 308 determines the prompted peoplecount and the heat blob count values are not equal. For example, thecomparator 308 may determine the prompted people count is less than theheat blob count. In such an example, the people meter controller 302 didnot receive user input from every person in the media presentationenvironment 104. In this example, the example comparator 308 triggersthe people meter controller 302 to generate a prompting message foradditional member logging. As used herein, member logging occurs when agiven panelist member logs into their respective input device 122 (orinto a common input device 122 used in the environment) to indicate thatthey are viewing the media. The example people meter controller 302generates the prompting message in an effort to obtain a response toverify the heat blob count and generate accurate audience monitoringdata.

In some examples, the comparator 308 determines the people count isgreater than the heat blob count. In such an example, the people metercontroller 302 obtained a false number of people viewing the media. Forexample, one of the panelists 106, 107, 108 may incorrectly respond to aprompt with information corresponding to a different panelist and thenattempt to correct the mistake by also entering their own information.As another example, a panelist member previously viewing the media mayhave left the media presentation environment 104 without logging outand/or indicating that they have stepped away. In some such examples,the comparator 308 triggers the people meter controller 302 to generatea prompting message to attempt to correct the discrepancy between theprompted people count and the heat blob count values. Additionally oralternatively, the example comparator 308 may decrease the people countvalue to equal the heat blob count value.

The example comparator 308 provides the verified people count to theexample people identification model controller 310. In some examples,the comparator 308 determines a time of the comparison between thepeople count and the heat blob count and provides the time to the peopleidentification model controller 310. For example, the comparator 308 mayidentify 4:58 pm as the time corresponding to the comparison between thepeople count and the heat blob count. In some examples, the comparator308 updates, or otherwise trains, the people identification model whenthe comparator 308 provides the verified people count and the timecorresponding to the comparison to the people identification modelcontroller 310.

The example people identification model controller 310 of theillustrated example of FIG. 3 trains a people identification model basedon data obtained from the example comparator 308, the example peoplemeter controller 302, and the example thermal image detector 306. Insome examples, the people identification model controller 310 is incommunication with the example media measurement data controller 212 ofFIG. 2. For example, the people identification model controller 310obtains information from the media measurement data controller 212 andprovides information to the media measurement data controller 212. Insome examples, the data obtained from the media measurement datacontroller 212 by the people identification model controller 310includes media identifying information. For example, the peopleidentification model controller 310 queries the media measurement datacontroller 212 for media identifying information at the timecorresponding to the comparison. In some examples, the media measurementdata controller 212 provides the data to the people identification modelcontroller 310 without receiving a request and/or query. In this manner,the people identification model controller 310 obtains the people countat, for example, 4:58 pm and obtains the broadcast channel airing on themedia device 110 at 4:58 pm. In some examples, the people identificationmodel controller 310 utilizes the data from one or more of the peoplemeter controller 302, the thermal image detector 306, the comparator308, and the media measurement data controller 212 to train a model topredict a verified people count for particular dates and times.

In some examples, the people identification model controller 310 passesthe verified people count to the media measurement data controller 212.For example, the people identification model controller 310 obtains theverified people count, packages the information, and provides theinformation to the media measurement data controller 212 for generationof exposure data. For example, the media measurement data controller 212utilizes the information to correlate the verified people count with themedia identifying information. In some examples, the peopleidentification model controller 310 obtains demographic information fromthe people meter controller 302 to pass to the media measurement datacontroller 212. For example, the people meter controller 302 determinesthe demographic information corresponding to the panelists logged intothe meter 102. In this manner, the example people identification modelcontroller 310 passes the people count, the demographic information, andthe time corresponding to the comparison to the media measurement datacontroller 212 to generate exposure data. The example peopleidentification model controller 310 is described in further detail belowin connection with FIG. 5.

The example model database 312 of the illustrated example of FIG. 3stores people identification models generated by the peopleidentification model controller 310. For example, the model database 312may periodically or aperiodically receive new, updated, and/or trainedpeople identification models. In some examples, the model database 312stores one people identification model. In some examples, the modeldatabase 312 stores multiple people identification models. The examplemodel database 312 is utilized for subsequent retrieval by the peoplemeter controller 302, the thermal image detector 306, and/or the peopleidentification model controller 310.

FIG. 4 is a block diagram illustrating an example implementation of thethermal image detector 306 of FIG. 3. The example thermal image detector306 of FIG. 3 includes an example thermal image database 402, an exampleheat blob determination controller 404, an example scanning controller406, and an example blob counter 408.

The example thermal image database 402 of the illustrated example ofFIG. 4 stores frames of thermal image data obtained from the exampleinterface 304 of FIG. 3. For example, the thermal image database 402stores the frames of thermal image data captured by the example thermalimaging sensor 124 for current and/or subsequent use by the heat blobdetermination controller 404. In some examples, the thermal imagedatabase 402 stores tagged and/or analyzed frames of thermal image data.

The example heat blob determination controller 404 of the illustratedexample of FIG. 4 obtains a frame of the thermal image data from theexample thermal image database 402. The example heat blob determinationcontroller 404 analyzes the frame for human sized heat blobs. In anexample operation, the heat blob determination controller 404 identifiesportions of the frame that warrant further attention. For example, theheat blob determination controller 404 detects warm areas of the framethat are to be analyzed further. A warm area of the frame can bedetermined based on the color of the pixels in that area. In someexamples, the heat blob determination controller 404 includesconfiguration data corresponding to the color scale of the thermal imagedata. The example heat blob determination controller 404 utilizes thecolor scale to identify temperature associated with a pixel color. Insome examples, the configuration data is specific to the particularthermal imaging sensor 124. For example, the configuration data may bebased on whether the thermal imaging sensor 124 includes an imageprocessor with greyscale imaging, colored imaging, etc. For example, theconfiguration data for an example greyscale implementation heat blobdetermination controller 404 may specify that a white-colored pixel isassociated with a first temperature, a lighter grey-colored pixel isassociated with a second temperature lower than the first temperature,and a black-colored pixel is associated with a third temperature lowerthan both the first and second temperatures. Configuration data for anexample color scale implementation of the heat blob determinationcontroller 404 may specify that a red-colored pixel is associated with afirst temperature, an orange-colored pixel is associated with a secondtemperature low than the first temperature, and a yellow-colored pixelis associated with a third temperature low than both the first andsecond temperatures.

In further operation, the heat blob determination controller 404 detectsfeatures of the frame that are incompatible with the presence of ahuman, or a small group of humans, and discards those features. Forexample, the heat blob determination controller 404 identifies coolareas of the frame, areas with hard edges, etc. By discarding theincompatible features, the example heat blob determination controller404 improves the probability and accuracy of detecting a human size heatblob.

The example heat blob determination controller 404 filters theidentified areas of the frame by applying symmetry, size, and/ordistance constraints. For example, the heat blob determinationcontroller 404 utilizes convolutional filtering, where a filter that isindicative of a shape, size, and/or distance is convolved over theidentified areas of the frame. In such an example, edge detectiontechniques may be utilized to identify an area of the frame thatcorresponds to an edge (e.g., an outline of a human head, arm, leg,etc.), where the identified area is convolved with one or more filters.For example, the heat blob determination controller 404 may implementstep detection, change detection, and any other edge detectiontechniques to identify an area of the frame corresponding to an edge.The example heat blob determination controller 404 filters theidentified areas of the frame to detect lines and features thatcorrespond to the shape of a human. In some examples, the heat blobdetermination controller 404 performs multiple filtering techniques withmultiple filters.

In some examples, the filters of the heat blob determination controller404 are calibrated based on the location and/or positioning of thethermal imaging sensor 124 in the media monitoring environment 104. Forexample, the heat blob determination controller 404 includes positioninginformation of the thermal imaging sensor 124. Such positioninginformation can be used to determine distance between the thermalimaging sensor 124 and an area of interested in a thermal image frame.In some examples, the filters are adjusted to accommodate for thedetermined distance. For example, if the heat blob determinationcontroller 404 determines an area of interest was captured x feet awayfrom the thermal imaging sensor 124, then the filters are adjusted(e.g., tuned, updated, etc.) to include reduced and/or increased sizedshapes (e.g., based on the value of x).

In some examples, the heat blob determination controller 404 fusesand/or otherwise connects two or more outputs of the filter detectionfor evaluation of the frame. For example, the connected outputs includeinformation indicative of one or more shapes in the frame. In someexamples, neural networks, adaptive boosting, and/or other types ofmodels are used to evaluate the connected (e.g., fused) outputs of thefilter detection. After evaluation, the example heat blob determinationcontroller 404 identifies one or more human size heat blobs. In someexamples, the heat blob determination controller 404 outputs theevaluation to the example blob counter 408.

In some examples, the heat blob determination controller 404 does notdetect human size heat blobs in the frame. In such an example, the heatblob determination controller 404 may send a trigger to the scanningcontroller 406 to prompt the scanning controller to capture more framesof thermal image data of the media presentation environment 104. Forexample, the thermal image detector 306 may be initiated to detect heatblobs (e.g., heat representation of the exterior temperature of anobject or person) when the media device 110 is turned on. In suchexamples, when the media device 110 is turned on, an audience isgenerally present. If the example heat blob determination controller 404does not detect human size heat blobs, then a recapture of theenvironment is to occur. For example, a panelist member may have brieflyleft the room after turning on the media device 110, the thermal imagingsensor 124 may have not captured the media presentation environment 104when the media device 110 was turned on, etc.

In some examples, the heat blob determination controller 404 providesthe connected output and/or evaluation results to the example peopleidentification model controller 310. The example people identificationmodel controller 310 utilizes the connected output and/or evaluationresults as training data for predicting a people count of the mediapresentation environment 104.

The example blob counter 408 of the illustrated example of FIG. 4obtains the evaluation output from the heat blob determinationcontroller 404 corresponding to a number, if any, of human size heatblobs. For example, the heat blob determination controller 404 providesinformation indicative of the evaluation of the frame of thermal imagedata to the blob counter 408. The example blob counter 408 includes acounter, such as a device which stores a number of times a heat blobcorresponds to a human. If the example blob counter 408 determines thathuman size heat blobs were detected, then the example blob counter 408increments the counter to the number of heat blobs that were detected.For example, if the heat blob determination controller 404 detected fivehuman size heat blobs, the blob counter 408 stores a count of five heatblobs. If the example blob counter 408 does not receive informationindicative of a detection of human size heat blobs, then the exampleblob counter 408 updates the blob counter with a count of zero.

In some examples, the blob counter 408 tags the frame of thermal imagedata with the heat blob count. For example, the frame includes metadataindicative of a time the frame was captured, the size of the frame, thedevice that captured the frame, etc., and further includes the heat blobcount appended by the blob counter 408. In some examples, the taggedframe is stored in the thermal image database 402 and/or provided to theexample comparator 308 and the example people identification modelcontroller 310.

In some examples, the thermal image database 402, the heat blobdetermination controller 404, the scanning controller 406, the blobcounter 408, and/or otherwise the thermal image detector 306 may becoupled to the people identification model controller 310 of FIG. 3 inan effort to provide training data (e.g., people monitoring data) to thepeople identification model. In some examples, it is beneficial toprovide training data (e.g., people monitoring data) to the peopleidentification model controller 310 to train the people identificationmodel to predict heat blob counts at particular times throughout theday.

Turning to FIG. 5, a block diagram illustrating the example peopleidentification model controller 310 of FIG. 3 is depicted to train amodel to learn about the presence of an audience of a household mediapresentation environment. The example people identification modelcontroller 310 includes an example communication controller 502, anexample feature extractor 504, an example model trainer 506, an examplemodel updater 508, and an example model generator 510.

The example communication controller 502 of the illustrated example ofFIG. 5 obtains information from the example comparator 308 of FIG. 3 topass to the example media measurement data controller 212 of FIG. 2. Theexample communication controller 502 is communicatively coupled to theexample comparator 308, the example media measurement data controller212, and the example feature extractor 504. In some examples, thecommunication controller 502 provides the people count to the mediameasurement data controller 212. For example, the comparator 308provides an updated and/or accurate count of the people (e.g., panelists106, 107, 108) viewing media in the media presentation environment 104.An updated and/or accurate count of the people is a verified count ofpeople based on the comparison between the people count from the peoplemeter controller 302 of FIG. 3 and the heat blob count from the thermalimage detector 306 of FIG. 3. In some examples, the communicationcontroller 502 obtains demographic data from the people meter controller302, indicative of the demographics of the people in the mediapresentation environment 104, and provides them to the media measurementdata controller 212.

The example feature extractor 504 of the illustrated example of FIG. 5extracts feature from information obtained from the example comparator308 of FIG. 3, the example people meter controller 302 of FIG. 3, andthe media measurement data controller 212 of FIG. 2. For example, thefeature extractor 504 obtains people monitoring data, over time, fromone or more of the comparator 308, the thermal image detector 306, thepeople meter controller 302, and the media measurement data controller212, and generates a feature vector corresponding to the peoplemonitoring data. In some examples, the feature extractor 504 obtainsmultiple instances of people monitoring data before generating a featurevector. For example, the feature extractor 504 obtains people monitoringdata over the span of a week. The example feature extractor 504generates or builds derived values of feature vectors (e.g.,representative of features in the people monitoring data) that are to beinformative and non-redundant to facilitate the training phase of thepeople identification model controller 310. As used herein, a featurevector is an n-dimensional array (e.g., a vector) of features thatrepresent some physical environment, media display, measurementparameter, etc. For example, a feature vector represents descriptivecharacteristics of the media presentation environment at a particulardate and time.

In the illustrated example of FIG. 5, the feature vector can identifythe number of people accounted for in the media presentation environment104 in addition to the time at which they were accounted, and the mediadisplayed at the time for which they were accounted. The feature vectorprovided by the feature extractor 504 facilitates the model trainer 506in training a people identification model to detect a people count for atime and a media type. For example, at time t1 on date X in the mediapresentation environment 104, the feature extractor 504 extracts dataindicative of media identifying information for a broadcast of “ABC theBachelor,” as well as data indicative that three are viewing thebroadcast.

In some examples, these extracted features, by themselves, may havelimited usefulness, because there is just one such feature event in agiven instance of people monitoring data. However, if the featureextractor 504 extracts feature data from multiple instances of peoplemonitoring data, the generated feature vector may be sufficient to trainthe people identification model. For example, the feature extractor 504extracts feature data having date X, Y, and Z at the time t1, indicativeof media identifying information for the broadcast of “ABC the Bachelor”and indicative that three people are viewing the broadcast. In such anexample, the model trainer 506 can utilize the feature vector to trainthe people identification model to predict the people count for time t1.

The example model trainer 506 of the illustrated example of FIG. 5trains the people identification model based on the output featurevector of the feature extractor 504. The model trainer 506 operates in atraining mode where it receives multiple instances of people monitoringdata, generates a prediction, and outputs a people identification modelbased on that prediction. For the example model trainer 506 to generatea people identification model, the model trainer 506 receives featurevectors corresponding to actual representations of the mediapresentation environment 104. For example, during a training mode,verifications are made about the people count of the media presentationenvironment 104 so that the data they provide to the comparator 308 issuitable for learning. For example, the model trainer 506 receives afeature vector indicative of the features of an actual mediapresentation environment and identifies a pattern in the features thatmaps the dates and times of the actual media presentation environment tothe people count and outputs a model that captures these daily and/orweekly patterns. The example model trainer 506 provides the outputpeople identification model to the example model updater 508 to assistin generating predictions about the people count at subsequent dates andtimes.

The example model updater 508 of the illustrated example of FIG. 5 flagsa people identification model received from the model trainer 506 as newand/or updated. For example, the model updater 508 can receive a peopleidentification model from the model trainer 506 that provides aprediction algorithm to detect a number of people in the mediapresentation environment 104. The model updater 508 determines that apeople identification model of this type is new and, therefore, tags itas new. In some examples, the model updater 508 determines that a peopleidentification model of this type has been generated previously and,therefore, will flag the model most recently generated as updated. Theexample model updater 508 provides the new and/or updated model to themodel generator 510.

The example model generator 510 of the illustrated example of FIG. 5generates a people identification model for publishing. For example, themodel generator 510 may receive a notification from the model updater508 that a new and/or updated people identification model has beentrained and the model generator 510 may create a file in which thepeople identification model is published so that the peopleidentification model can be saved and/or stored as the file. In someexamples, the model generator 510 provides a notification to the peoplemeter controller 302 and/or the thermal image detector 306 that a peopleidentification model is ready to be transformed and published. In someexamples, the model generator 510 stores the people identification modelin the example model database 312 for subsequent retrieval by the peoplemeter controller 302.

In some examples, the people identification model controller 310determines a people identification model is trained and ready for usewhen the prediction meets a threshold amount of error. In some examples,the people meter controller 302 and/or the thermal image detector 306implements the trained people identification model to count people in amedia presentation environment. Alternatively, the example peopleidentifier 210 implements the people identification model. In such anexample, the people identification model would obtain thermal image datafrom the thermal imaging sensor 124 to make informed decisions about thepeople count, without the use of audience input data. In this manner,the people identification model may replace the people meter controller302, the thermal image detector 306, and the comparator 308.

While an example manner of implementing the people identifier 210 ofFIG. 2 is illustrated in FIGS. 3, 4, and 5, one or more of the elements,processes and/or devices illustrated in FIGS. 3, 4, and 5 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example people meter controller 302, theexample interface 304, the example thermal image detector 306, theexample comparator 308, the example people identification modelcontroller 310, the example model database 312, the example thermalimage database 402, the example heat blob determination controller 404,the example scanning controller 406, the example blob counter 408, theexample communication controller 502, the example feature extractor 504,the example model trainer 506, the example model updater 508, theexample model generator 510, and/or, more generally, the example peopleidentifier 210 of FIG. 2 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example people meter controller 302, theexample interface 304, the example thermal image detector 306, theexample comparator 308, the example people identification modelcontroller 310, the example model database 312, the example thermalimage database 402, the example heat blob determination controller 404,the example scanning controller 406, the example blob counter 408, theexample communication controller 502, the example feature extractor 504,the example model trainer 506, the example model updater 508, theexample model generator 510, and/or, more generally, the example peopleidentifier 210 could be implemented by one or more analog or digitalcircuit(s), logic circuits, programmable processor(s), programmablecontroller(s), graphics processing unit(s) (GPU(s)), digital signalprocessor(s) (DSP(s)), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example people metercontroller 302, the example interface 304, the example thermal imagedetector 306, the example comparator 308, the example peopleidentification model controller 310, the example model database 312, theexample thermal image database 402, the example heat blob determinationcontroller 404, the example scanning controller 406, the example blobcounter 408, the example communication controller 502, the examplefeature extractor 504, the example model trainer 506, the example modelupdater 508, and/or the example model generator 510, is/are herebyexpressly defined to include a non-transitory computer readable storagedevice or storage disk such as a memory, a digital versatile disk (DVD),a compact disk (CD), a Blu-ray disk, etc. including the software and/orfirmware. Further still, the example people identifier 210 of FIG. 2 mayinclude one or more elements, processes and/or devices in addition to,or instead of, those illustrated in FIGS. 3, 4, and 5, and/or mayinclude more than one of any or all of the illustrated elements,processes and devices. As used herein, the phrase “in communication,”including variations thereof, encompasses direct communication and/orindirect communication through one or more intermediary components, anddoes not require direct physical (e.g., wired) communication and/orconstant communication, but rather additionally includes selectivecommunication at periodic intervals, scheduled intervals, aperiodicintervals, and/or one-time events.

Flowcharts representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the people identifier 210 of FIGS.3, 4, and 5 are shown in FIGS. 6-10. The machine readable instructionsmay be one or more executable programs or portion(s) of an executableprogram for execution by a computer processor such as the processor 1112shown in the example processor platform 1100 discussed below inconnection with FIG. 11. The programs may be embodied in software storedon a non-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associatedwith the processor 1112, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor1112 and/or embodied in firmware or dedicated hardware. Further,although the example programs are described with reference to theflowcharts illustrated in FIGS. 6-10, many other methods of implementingthe example people identifier 210 may alternatively be used. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.Additionally or alternatively, any or all of the blocks may beimplemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

The machine readable instructions described herein may be stored in oneor more of a compressed format, an encrypted format, a fragmentedformat, a compiled format, an executable format, a packaged format, etc.Machine readable instructions as described herein may be stored as data(e.g., portions of instructions, code, representations of code, etc.)that may be utilized to create, manufacture, and/or produce machineexecutable instructions. For example, the machine readable instructionsmay be fragmented and stored on one or more storage devices and/orcomputing devices (e.g., servers). The machine readable instructions mayrequire one or more of installation, modification, adaptation, updating,combining, supplementing, configuring, decryption, decompression,unpacking, distribution, reassignment, compilation, etc. in order tomake them directly readable, interpretable, and/or executable by acomputing device and/or other machine. For example, the machine readableinstructions may be stored in multiple parts, which are individuallycompressed, encrypted, and stored on separate computing devices, whereinthe parts when decrypted, decompressed, and combined form a set ofexecutable instructions that implement a program such as that describedherein.

In another example, the machine readable instructions may be stored in astate in which they may be read by a computer, but require addition of alibrary (e.g., a dynamic link library (DLL)), a software development kit(SDK), an application programming interface (API), etc. in order toexecute the instructions on a particular computing device or otherdevice. In another example, the machine readable instructions may needto be configured (e.g., settings stored, data input, network addressesrecorded, etc.) before the machine readable instructions and/or thecorresponding program(s) can be executed in whole or in part. Thus, thedisclosed machine readable instructions and/or corresponding program(s)are intended to encompass such machine readable instructions and/orprogram(s) regardless of the particular format or state of the machinereadable instructions and/or program(s) when stored or otherwise at restor in transit.

The machine readable instructions described herein can be represented byany past, present, or future instruction language, scripting language,programming language, etc. For example, the machine readableinstructions may be represented using any of the following languages: C,C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language(HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example processes of FIGS. 6-10 may beimplemented using executable instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”,etc.) do not exclude a plurality. The term “a” or “an” entity, as usedherein, refers to one or more of that entity. The terms “a” (or “an”),“one or more”, and “at least one” can be used interchangeably herein.Furthermore, although individually listed, a plurality of means,elements or method actions may be implemented by, e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent examples or claims, these may possibly be combined, and theinclusion in different examples or claims does not imply that acombination of features is not feasible and/or advantageous.

FIGS. 6, 7, 8, and 9 illustrate programs that are executed by theexample people identifier 210 to determine an accurate people countutilizing audience input and thermal image data. FIG. 6 illustrates anexample people count program 600 implemented by the example people metercontroller 302 of FIG. 3 to determine a people count based on audienceinput. FIG. 7 illustrates an example heat blob count program 700implemented by the example thermal image detector 306 of FIG. 3 todetermine a heat blob count based on thermal image data from the thermalimaging sensor 124 of FIG. 1. FIG. 8 illustrates further detail of theheat blob count program 700 to determine a heat blob count based onthermal image data provided by the thermal imaging sensor 124. FIG. 9illustrates a comparison program 900 implemented by the examplecomparator 308 of FIG. 3 to determine an accurate people count of amedia presentation environment.

Turning to FIG. 6, the example people count program 600 begins at block602, when the example media device 110 (FIG. 1) is on. In some examples,the people count program 600 begins when a panelist member (e.g.,panelist 106, 107, 108) activates their respective input device 122,and/or when a scheduling interval has elapsed for the people metercontroller 302 (FIG. 3) to prompt panelists for verification of viewing.

The example people meter controller 302 obtains user input (block 604).For example, the people meter controller 302 waits to receive anindication from an input device 122 that a panelist member is logged on.In some examples, when a panelist member is logged on, the panelistmember is indicating that they are viewing media in the mediapresentation environment 104 (FIG. 1). In some examples, the panelistmembers possess input devices 122 that include a unique identifiercorresponding to the panelist.

The example people meter controller 302 determines a people count basedon the user input (block 606). For example, the people meter controller302 counts the number of unique identifiers received from the inputdevice(s) 122. The example people meter controller 302 provides thepeople count to the example comparator 308 (FIG. 3). The example peoplecount program 600 of FIG. 6 ends when the comparator 308 obtains thepeople count. In some examples, the people count program 600 repeatswhen the example media device 110 is turned on, when a panelist member(e.g., panelist 106, 107, 108) activates their respective input device122, and/or when an interval has elapsed for people meter controller 302(FIG. 3) to prompt panelists for verification of viewing.

Turning to FIG. 7, the example heat blob count program 700 begins whenthe example media device 110 (FIG. 1) is on (block 702). For example,the thermal imaging sensor 124 (FIG. 1) may be activated and begincapturing thermal image data when the media device 110 is turned on, andthus the heat blob count program 700 begins.

The example thermal image database 402 (FIG. 4) obtains frames ofthermal image data from the example thermal imaging sensor 124 (block704). For example, the thermal imaging sensor 124 captures frames ofthermal image data corresponding to the media presentation environment104 and stores them in the thermal image database 402. The example heatblob determination controller 404 (FIG. 4) and the example blob counter408 (FIG. 4) determine a heat blob count based on the thermal image data(block 706). For example, the heat blob determination controller 404 andthe blob counter 408 operate to analyze the frames of thermal image dataand determine a number of human size heat blobs illustrated in theframes. Further example instructions that may be used to implement block706 are described below in connection with FIG. 8.

The example blob counter 408 provides the heat blob count to thecomparator 308 (FIG. 3) (block 708). For example, the blob counter 408is communicatively coupled to the comparator 308, and further providesthe heat blob count to the comparator 308 for a comparison to theprompted people count. The heat blob count program 700 ends when theexample blob counter 408 provides the heat blob count to the examplecomparator 308. The heat blob count program 700 repeats when the examplescanning controller 406 (FIG. 4) initiates a new capture of thermalimage data. For example, the scanning controller 406 may initiate thethermal imaging sensor 124 to capture frames of the media presentationenvironment 104, and thus a new and/or same number of heat blobs, in themedia presentation environment 104, may be detected.

Turning to FIG. 8, the example heat blob determination program 706begins when the heat blob determination controller 404 (FIG. 4) obtainsa frame of thermal image data from the thermal image database 402 (FIG.4) (block 802). For example, the heat blob determination controller 404queries the thermal image database 402 for frames corresponding to themedia presentation environment 104.

The example heat blob determination controller 404 identifies portionsof the frame that include a high probability of a human (block 804). Forexample, the heat blob determination controller 404 detects warm areasof the frame that are to be analyzed further, as described above. Theexample heat blob determination controller 404 detects features of theframe that are incompatible with the presence of a human (block 806).For example, the heat blob determination controller 404 identifies coolareas of the frame, areas with hard edges, etc., as described above. Theexample heat blob determination controller 404 discards the incompatiblefeatures (block 810). For example, the heat blob determinationcontroller 404 removes any groups of pixels that were determined to beincompatible with the presence of a human in an effort to increase theprobability and accuracy of detecting human size heat blob(s).

The example heat blob determination controller 404 applies filters tothe identified portions of the frame (block 812). For example, the heatblob determination controller 404 convolves filters indicative of ashape, size, and/or distance over the identified portions of the frameto detect lines and features that correspond to the shape of a human.The example the heat blob determination controller 404 connects theoutput(s) of the filters to form a fully connected output (block 814).

For example, the heat blob determination controller 404 concatenates theoutputs of the filters to form the fully connected output layer. Thefully connected output layer is indicative of the number of human sizeand/or human shaped heat blobs detected during the filtering. In thismanner, the example heat blob determination controller 404 evaluates thefully connected output (block 816). For example, the heat blobdetermination controller 404 utilizes the information in the fullyconnected output to predict and/or otherwise determine if a human sizeheat blob was detected.

The example heat blob determination controller 404 determines if humansize heat blobs were detected (block 818). For example, the heat blobdetermination controller 404 utilizes the evaluation of the fullyconnected output to determine if human size heat blobs were detected. Ifthe example heat blob determination controller 404 determines human sizeheat blobs were not detected (e.g., block 818=NO), then the example heatblob determination controller 404 prompts the scanning controller 406 toscan the media environment (block 820). For example, the heat blobdetermination controller 404 may send a trigger to the scanningcontroller 406 to prompt the scanning controller to capture more framesof thermal image data of the media presentation environment 104.

If the example heat blob determination controller 404 determines humansize heat blobs were detected (e.g., block 818=YES), then the exampleheat blob determination controller 404 provides the fully connectedoutput to example blob counter 408. The example blob counter 408determines the number of human size heat blobs in the fully connectedoutput (block 822). For example, the blob counter 408 analyzesinformation in the fully connected output to determine the number ofhuman size heat blobs that were identified in the frame.

The example blob counter 408 updates the counter with the number of heatblobs (block 824). For example, the blob counter 408 increments thecounter to equal the number of human size heat blobs detected in theimage frame. The example heat blob determination program 706 ends whenthe blob counter 408 updates the counter value to equal the number ofhuman size heat blobs detected in the image frame. The example heat blobdetermination program 706 repeats when the example heat blobdetermination controller 404 obtains a new frame of thermal image datafrom the example thermal image database 402.

Turning to FIG. 9, the example comparison program 900 begins when theexample comparator 308 (FIG. 3) obtains the people count (block 902).For example, the comparator 308 obtains the prompted people count fromthe example people meter controller 302 (FIG. 3). Additionally, theexample comparator 308 obtains the heat blob count (block 904). Forexample, the comparator 308 obtains the heat blob count from the examplethermal image detector 306 (FIG. 3).

The example comparator 308 compares the prompted people count to theheat blob count (block 906). For example, the comparator 308 comparesthe value of the prompted people count to the value of the heat blobcount. The example comparator 308 determines if the counts match (block908). For example, the comparator 308 determines if the count values areequal in value. If the example comparator 308 determines the countsmatch (e.g., block 908=YES), the example comparator 308 determines atime of the comparison (block 910). For example, the comparator 308identifies when the prompted people count was compared to the heat blobcount in order to provide the media measurement data controller 212(FIG. 2) with enough information to correlate the people count withmedia identifying information.

In the illustrated example, the comparator 308 provides the people countand the time to the people identification model (block 912). Forexample, the comparator 308 outputs information determined from thecomparison to train the people identification model. Further, theexample comparator 308 sends a reset notification to the example peoplemeter controller 302 (block 914). For example, the comparator 308notifies the example people meter controller 302 to reset the schedulinginterval timers that determine when prompting messages are to betriggered. In some examples, when the comparator 308 provides thenotification to the people meter controller 302, the example comparisonprogram 900 ends.

Returning to block 908, if the example comparator 308 determines thecounts do not match (e.g., block 908=NO), the example comparator 308determines if the comparison is indicative that the prompted peoplecount is less than the blob count (block 916). For example, if thepeople meter controller 302 did not receive user and/or audience inputfrom every input device 122 and/or from the input device 122 in themedia presentation environment 104, then the comparison is indicativethat the people count is less than the blob count (e.g., block 916=YES).In this manner, the example comparator 308 initiates a prompt foradditional member logging (block 918). For example, the comparator 308triggers the people meter controller 302 to generate a prompting messagefor additional member logging. In this manner, the example people metercontroller 302 generates the prompting message in an effort to obtain aresponse from the panelists to verify the heat blob count and generateaccurate audience monitoring data.

When the comparator 308 initiates a prompt for additional member logging(block 918), control advances to block 910. The machine-readableinstructions of blocks 910, 912, and 914 are further executed by theexample comparator 308.

In some examples, if the comparison is not indicative that the peoplecount is less than the blob count (e.g., block 916=NO), the comparator308 determines the comparison is indicative that the people count isgreater than the blob count (block 920). In such an example, the peoplemeter controller 302 obtained a false number of people viewing themedia. For example, a panelist member may have overcompensated for theirpresence in the media presentation environment 104, such as by loggingin when not actually present, or accidentally logging in as a differentperson. The example comparator 308 initiates a prompt for removal ofmember logging (block 922). For example, the comparator 308 triggers thepeople meter controller 302 to generate a prompting message indicativeto remove a panelist member.

When the example comparator 308 provides the notification to the examplepeople meter controller 302, control returns to block 910 and themachine-readable instructions of block 910, 912, and 914 are executed bythe example comparator 308. The example comparison program 900 ends whenthe comparator 308 sends the reset notification to the people metercontroller 302. The example comparison program 900 is repeated when theexample comparator 308 obtains a new people count and/or a new blobcount from either of the example people meter controller 302 or thethermal image detector 306.

FIG. 10 illustrates an example training program 1000 to train a peopleidentification model to predict a verified people count for subsequentdates and times in the media presentation environment 104. The examplemachine readable instructions 1000 may be used to implement the examplepeople identification model controller 310 of FIG. 3. In FIG. 10, theexample training program 1000 beings at block 1002, when the examplefeature extractor 504 (FIG. 5) obtains comparison data from the examplecomparator 308 (FIG. 3). For example, the comparator 308 providescomparison results and time stamps to the example feature extractor 504.

The example feature extractor 504 obtains data from the example peoplemeter controller 302 (FIG. 3) (block 1004). For example, the peoplemeter controller 302 provides demographic data corresponding to thelogged in panelist members at a time they logged in. The example featureextractor 504 obtains evaluation data from the example thermal imagedetector 306 (FIG. 3) (block 1006). For example, the thermal imagedetector 306 provides the analysis and evaluation results of the frameof thermal image data for a particular time. Additionally, the examplethermal image detector 306 provides the tagged frame (e.g., the frametagged with a blob count by the blob counter 408 (FIG. 4)) to theexample feature extractor 504.

The example feature extractor 504 obtains media identifying informationfrom the example media measurement data controller 212 (FIG. 2) (block1008). For example, the media measurement data controller 212 providesmedia identifying information to the communication controller 502 (FIG.5) in response to receiving a people count, and the communicationcontroller 502 provides the media identifying information to the featureextractor 504.

The example feature extractor 504 extracts features of the peoplemonitoring information (block 1010). As used herein, the peoplemonitoring information corresponds to the information and data obtainedfrom the example people meter controller 302, the example comparator308, the example thermal image detector 306, and the example mediameasurement data controller 212. This data can be used to determine averified people count and/or represents a verified people count.

The example feature extractor 504 generates a feature vectorcorresponding to the extracted features of the people monitoring data(block 1012). For example, the feature extractor 504 generates a featurevector that represents descriptive characteristics of a physicalenvironment (e.g., the media presentation environment) at particulardates and times, or at a particular date and time. The example featureextractor 504 determines if there are additional people monitoring data(block 1014). For example, the feature extractor 504 determines ifanother set of people monitoring data, representative of the peoplecount during a different time in the media presentation environment 104,is available. If the example feature extractor 504 determines there areadditional people monitoring data (block 1014=YES), then control returnsto block 1002. In such an example, the model trainer 506 (FIG. 5) needsto receive people monitoring data of the media presentation environment104 that is sufficient to generate a sufficiently accurate and/orprecise model.

If the example feature extractor 504 determines there are not additionalpeople monitoring data (block 1014=NO), then the example model trainer506 trains the people identification model based on the feature vector(block 1016). For example, the model trainer 506 may utilize a machinelearning technique to predict output probability values corresponding tothe number of people in the media presentation environment 104. Theoutput probability values could correspond to future predictions of thenumber of people viewing particular media in the media presentationenvironment 104 or the output probability values could correspond tofuture predictions of the number of people in the media presentationenvironment 104 at a particular hour of the day or day of the week.

After the people identification model has been trained, the examplemodel updater 508 flags the people identification model as new orupdated. Further, the example model generator 510 generates the trainedmodel (block 1018). For example, the model generator 510 receives thenew and/or updated trained people identification model from the modelupdater 508 and generates a file to store/save the trained peopleidentification model for subsequent access by the people metercontroller 302 (FIG. 3) and/or the thermal image detector 306 (FIG. 3).

The example model generator 510 stores the trained people identificationmodel in the example model database 312 (FIG. 3) (block 1020). Thetraining program 1000 ends when the trained people identification modelis stored in the example model database 312. The training program 1000repeats when the example feature extractor 504 obtains people monitoringdata.

In some examples, the trained people identification model is publishedby the people identification model controller 310. When the peopleidentification model is published, the people identification modeloperates in a detection phase, where the example people identificationmodel controller 310 utilizes the trained model, in real time, todetermine an accurate people count of the media presentation environment104. In some examples, the people identification model replaces thepeople meter controller 302, the thermal image detector 306, and thecomparator 308. In such an example, the people identification modelobtains input data from the thermal imaging sensor 124 to determine anaccurate people count of the media presentation environment 104. Suchinput from the thermal imaging sensor 124 includes frames of thermalimage data. For example, the people identification model utilizes itsprediction capabilities in connection with information obtained aboutthe thermal environment of the media presentation environment 104 tooutput an accurate representation of the number of people in the mediapresentation environment 104. In such an example, the people metercontroller 302 no longer requires audience input, and thus compliancebecomes less of an issue when determining an accurate people count.

FIG. 11 is a block diagram of an example processor platform 1100structured to execute the instructions of FIGS. 6-10 to implement thepeople identifier 210 of FIGS. 2, 3, 4, and 5. The processor platform1100 can be, for example, a server, a personal computer, a workstation,a self-learning machine (e.g., a neural network), a mobile device (e.g.,a cell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 1100 of the illustrated example includes aprocessor 1112. The processor 1112 of the illustrated example ishardware. For example, the processor 1112 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example people metercontroller 302, the example interface 304, the example thermal imagedetector 306, the example comparator 308, the example peopleidentification model controller 310, the example model database 312, theexample thermal image database 402, the example heat blob determinationcontroller 404, the example scanning controller 406, the example blobcounter 408, the example communication controller 502, the examplefeature extractor 504, the example model trainer 506, the example modelupdater 508, the example model generator 510.

The processor 1112 of the illustrated example includes a local memory1113 (e.g., a cache). In some examples, the local memory 1113 implementsthe example model database 312 and the example thermal image database402. The processor 1112 of the illustrated example is in communicationwith a main memory including a volatile memory 1114 and a non-volatilememory 1116 via a bus 1118. The volatile memory 1114 may be implementedby Synchronous Dynamic Random Access Memory (SDRAM), Dynamic RandomAccess Memory (DRAM), RAMBUS® Dynamic Random Access Memory (RDRAM®)and/or any other type of random access memory device. The non-volatilememory 1116 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 1114, 1116 iscontrolled by a memory controller.

The processor platform 1100 of the illustrated example also includes aninterface circuit 1120. The interface circuit 1120 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface. In someexamples, the interface circuit 1120 implements the example interface304.

In the illustrated example, one or more input devices 1122 are connectedto the interface circuit 1120. The input device(s) 1122 permit(s) a userto enter data and/or commands into the processor 1112. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a thermal image camera, akeyboard, a button, a mouse, a touchscreen, a track-pad, a trackball,isopoint and/or a voice recognition system.

One or more output devices 1124 are also connected to the interfacecircuit 1120 of the illustrated example. The output devices 1124 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, and/orspeaker. The interface circuit 1120 of the illustrated example, thus,typically includes a graphics driver card, a graphics driver chip,and/or a graphics driver processor.

The interface circuit 1120 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1126. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1100 of the illustrated example also includes oneor more mass storage devices 1128 for storing software and/or data.Examples of such mass storage devices 1128 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 1132 of FIGS. 6-10 may be stored inthe mass storage device 1128, in the volatile memory 1114, in thenon-volatile memory 1116, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that determinea people count in a media presentation environment by capturing framesof thermal image data and comparing an evaluation of the frames toaudience input data. The disclosed example methods, apparatus andarticles of manufacture improve the efficiency of using a computingdevice by using the audience input data and the evaluation of the framesto train a people identification model to determine the people count.The people identification model, once trained, can replace the peopleidentifier and thus, improve the efficiency processing time byeliminating a need for audience input data and human shape detection.The disclosed example methods, apparatus and articles of manufactureimprove the efficiency of using a computing device by reducing promptingmessages when the heat blob count matches the prompted people count andthus verifies the prompted people count. The disclosed methods,apparatus and articles of manufacture are accordingly directed to one ormore improvement(s) in the functioning of a computer.

Example methods, apparatus, systems, and articles of manufacture tocount people in a media environment are disclosed herein. Furtherexamples and combinations thereof include the following:

Example 1 includes an apparatus to count people in a media environment,the apparatus comprising a thermal image detector to determine a heatblob count based on a frame of thermal image data, the frame of thermalimage data captured in the media environment, a comparator to comparethe heat blob count to a prompted people count, the prompted peoplecount based on one or more responses to a prompting message, and whenthe heat blob count and the prompted people count match, cause a timerthat is to trigger generation of the prompting message to be reset.

Example 2 includes the apparatus of example 1, wherein the comparator isto cause the prompting message to be generated when the prompted peoplecount does not match the heat blob count.

Example 3 includes the apparatus of example 1, wherein when thecomparator determines the prompted people count is greater than the heatblob count, the comparator is to decrease the prompted people count toequal the heat blob count.

Example 4 includes the apparatus of example 1, further including apeople meter controller to determine the prompted people count based onthe one or more responses to the prompting message, the one or moreresponses associated with unique people identifiers.

Example 5 includes the apparatus of example 4, wherein the people metercontroller is to generate the prompting message to request a response toprovide an input or remove an input.

Example 6 includes the apparatus of example 1, wherein the thermal imagedetector is to evaluate features in the frame of thermal image data todetect one or more human size heat blobs, the one or more human sizeheat blobs corresponding to the heat blob count.

Example 7 includes the apparatus of example 1, wherein the comparator isto verify a number of people in the media environment based oncomparison of the heat blob count and the prompted people count.

Example 8 includes a method to count people in a media environment, themethod comprising determining a heat blob count based on a frame ofthermal image data, the frame of thermal image data captured in themedia environment, comparing the heat blob count to a prompted peoplecount, the prompted people count based on one or more responses to aprompting message, and when the heat blob count and the prompted peoplecount match, cause a timer that is to trigger generation of theprompting message to be reset.

Example 9 includes the method of example 8, wherein further includingcausing the prompting message to be generated when the prompted peoplecount does not match the heat blob count.

Example 10 includes the method of example 8, further includingdecreasing the prompted people count to equal the heat blob count whenthe prompted people count is greater than the heat blob count.

Example 11 includes the method of example 8, further includingdetermining the prompted people count based on the one or more responsesto the prompting message, the one or more responses associated withunique people identifiers.

Example 12 includes the method of example 11, further includinggenerating the prompting message to request a response to provide aninput or remove an input.

Example 13 includes the method of example 8, further includingevaluating features in the frame of thermal image data to detect one ormore human size heat blobs, the one or more human size heat blobscorresponding to the heat blob count.

Example 14 includes the method of example 8, further including verifyinga number of people in the media environment based on comparison of theheat blob count and the prompted people count.

Example 15 includes a non-transitory computer readable storage mediumcomprising instructions that, when executed, cause one or moreprocessors to at least determine a heat blob count based on a frame ofthermal image data, the frame of thermal image data captured in a mediaenvironment, compare the heat blob count to a prompted people count, theprompted people count based on one or more responses to a promptingmessage, and when the heat blob count and the prompted people countmatch, cause a timer that is to trigger generation of the promptingmessage to be reset.

Example 16 includes the non-transitory computer readable storage mediumof example 15, wherein the instructions, when executed, cause the one ormore processors to cause the prompting message to be generated when theprompted people count does not match the heat blob count.

Example 17 includes the non-transitory computer readable storage mediumof example 15, wherein the instructions, when executed, cause the one ormore processors to decrease the prompted people count to equal the heatblob count when the prompted people count is greater than the heat blobcount.

Example 18 includes the non-transitory computer readable storage mediumof example 15, wherein the instructions, when executed, cause the one ormore processors to determine the prompted people count based on the oneor more responses to the prompting message, the one or more responsesassociated with unique people identifiers.

Example 19 includes the non-transitory computer readable storage mediumof example 18, wherein the instructions, when executed, cause the one ormore processors to generate the prompting message to request a responseto provide an input or remove an input.

Example 20 includes the non-transitory computer readable storage mediumof example 15, wherein the instructions, when executed, cause the one ormore processors to evaluate features in the frame of thermal image datato detect one or more human size heat blobs, the one or more human sizeheat blobs corresponding to the heat blob count.

Example 21 includes the non-transitory computer readable storage mediumof example 15, wherein the instructions, when executed, cause the one ormore processors to verify a number of people in the media environmentbased on comparison of the heat blob count and the prompted peoplecount.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

The following claims are hereby incorporated into this DetailedDescription by this reference, with each claim standing on its own as aseparate embodiment of the present disclosure.

What is claimed is:
 1. An apparatus to count people in a mediaenvironment, the apparatus comprising: a thermal image detector todetermine a heat blob count based on a frame of thermal image data, theframe of thermal image data corresponding to the media environment; acomparator to: compare the heat blob count to a prompted people count,the prompted people count based on one or more responses to a promptingmessage; when the heat blob count and the prompted people count match,cause a timer that is to trigger generation of the prompting message tobe reset; and a controller to train a machine learning model based on afeature vector including a verified people count, at least one of a dayor time corresponding to the verified people count, and a media sourcedetected in the media environment during the at least one of the day ortime, the verified people count based on the comparison of the heat blobcount to the prompted people count, the machine learning model topredict subsequent people counts based on features representative of themedia environment.
 2. The apparatus of claim 1, wherein the comparatoris to cause the prompting message to be generated when the promptedpeople count does not match the heat blob count.
 3. The apparatus ofclaim 1, wherein when the comparator determines the prompted peoplecount is greater than the heat blob count, the comparator is to decreasethe prompted people count to equal the heat blob count.
 4. The apparatusof claim 1, wherein the controller is a first controller, and furtherincluding a second controller to determine the prompted people countbased on the one or more responses to the prompting message, the one ormore responses associated with unique people identifiers.
 5. Theapparatus of claim 4, wherein the second controller is to generate theprompting message to request a response to provide an input or remove aninput.
 6. The apparatus of claim 1, wherein the thermal image detectoris to evaluate features in the frame of thermal image data to detect oneor more human size heat blobs, the one or more human size heat blobscorresponding to the heat blob count.
 7. The apparatus of claim 1,wherein the comparator is to verify a number of people in the mediaenvironment based on comparison of the heat blob count and the promptedpeople count.
 8. A method to count people in a media environment, themethod comprising: determining a heat blob count based on a frame ofthermal image data, the frame of thermal image data captured in themedia environment; comparing the heat blob count to a prompted peoplecount, the prompted people count based on one or more responses to aprompting message; when the heat blob count and the prompted peoplecount match, causing a timer that is to trigger generation of theprompting message to be reset; and training a machine learning modelbased on a feature vector including a verified people count, at leastone of a day or time corresponding to the verified people count, and amedia source detected in the media environment during the at least oneof the day or time, the verified people count based on the comparison ofthe heat blob count to the prompted people count, the machine learningmodel to predict subsequent people counts based on featuresrepresentative of the media environment.
 9. The method of claim 8,wherein further including causing the prompting message to be generatedwhen the prompted people count does not match the heat blob count. 10.The method of claim 8, further including decreasing the prompted peoplecount to equal the heat blob count when the prompted people count isgreater than the heat blob count.
 11. The method of claim 8, furtherincluding determining the prompted people count based on the one or moreresponses to the prompting message, the one or more responses associatedwith unique people identifiers.
 12. The method of claim 11, furtherincluding generating the prompting message to request a response toprovide an input or remove an input.
 13. The method of claim 8, furtherincluding evaluating features in the frame of thermal image data todetect one or more human size heat blobs, the one or more human sizeheat blobs corresponding to the heat blob count.
 14. The method of claim8, further including verifying a number of people in the mediaenvironment based on comparison of the heat blob count and the promptedpeople count.
 15. A non-transitory computer readable storage mediumcomprising instructions that, when executed, cause one or moreprocessors to at least: determine a heat blob count based on a frame ofthermal image data, the frame of thermal image data captured in a mediaenvironment; compare the heat blob count to a prompted people count, theprompted people count based on one or more responses to a promptingmessage; when the heat blob count and the prompted people count match,cause a timer that is to trigger generation of the prompting message tobe reset; and train a machine learning model based on a feature vectorincluding a verified people count, at least one of a day or timecorresponding to the verified people count, and a media source detectedin the media environment during the at least one of the day or time, theverified people count based on the comparison of the heat blob count tothe prompted people count, the machine learning model to predictsubsequent people counts based on features representative of the mediaenvironment.
 16. The non-transitory computer readable storage medium ofclaim 15, wherein the instructions, when executed, cause the one or moreprocessors to cause the prompting message to be generated when theprompted people count does not match the heat blob count.
 17. Thenon-transitory computer readable storage medium of claim 15, wherein theinstructions, when executed, cause the one or more processors todecrease the prompted people count to equal the heat blob count when theprompted people count is greater than the heat blob count.
 18. Thenon-transitory computer readable storage medium of claim 15, wherein theinstructions, when executed, cause the one or more processors todetermine the prompted people count based on the one or more responsesto the prompting message, the one or more responses associated withunique people identifiers.
 19. The non-transitory computer readablestorage medium of claim 18, wherein the instructions, when executed,cause the one or more processors to generate the prompting message torequest a response to provide an input or remove an input.
 20. Thenon-transitory computer readable storage medium of claim 15, wherein theinstructions, when executed, cause the one or more processors toevaluate features in the frame of thermal image data to detect one ormore human size heat blobs, the one or more human size heat blobscorresponding to the heat blob count.
 21. The non-transitory computerreadable storage medium of claim 15, wherein the instructions, whenexecuted, cause the one or more processors to verify a number of peoplein the media environment based on comparison of the heat blob count andthe prompted people count.